One type of data that’s easier to find on the net is Weather data. Many sites provide historical data on many meteorological parameters such as pressure, temperature, humidity, wind_speed, visibility, etc. One such weather dataset we obtained is from kaggle. (Source URL: https://www.kaggle.com/muthuj7/weather-dataset)

The dataset has hourly temperature recorded 10 years starting from 2006-04-01 00:00:00.000 +0200 to 2016-09-09 23:00:00.000 +0200. It corresponds to Finland, a country in the Northern Europe.

In this assignment, we are responsible for performing data cleaning, perform analysis for testing the (given) Hypothesis.

The Null Hypothesis H0 is "Has the Apparent temperature and humidity compared monthly across 10 years of the data indicate an increase due to Global warming"

The H0 means we need to find whether the average Apparent temperature for the month of a month say April starting from 2006 to 2016 and the average humidity for the same period have increased or not. This monthly analysis has to be done for all 12 months over the 10 year period. So you are basically resampling your data from hourly to monthly, then comparing the same month over the 10 year period. Support your analysis by appropriate visualizations using matplotlib and / or seaborn library.